Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review

The cold chain is crucial to ensure the quality and effectiveness of transported and stored medicines. For this, it is necessary to carry out the thermal mapping of routes for drugs transported between 15°C and 30°C, so that the most assertive decision can be taken without raising costs. This study aims to identify the main factors influencing the thermal mapping of pharmaceutical products in the cold chain and applying the machine learning technique. The method used for this systematic review is the Prisma, where the identification, screening, eligibility, and inclusion stages were analyzed. After analyzing 75 articles, the result shows that only eight papers were consistent with the use of modeling in the medicine cold chain distribution. Thus, it can be concluded that there is an extensive field to be researched regarding the use of prediction algorithms in the cold chain of drugs and vaccines.

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Obfuscated computer virus detection using machine learning algorithm

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v8i4.1584 ◽

2019 ◽

Vol 8 (4) ◽

Author(s):

Tan Hui Xin ◽

Ismahani Ismail ◽

Ban Mohammed Khammas

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Virus Detection ◽

Computer Virus ◽

Machine Learning Technique ◽

Memory Space ◽

Alternative Approach ◽

Machine Learning Approach ◽

Learning Technique ◽

String Feature

Nowadays, computer virus attacks are getting very advanced. New obfuscated computer virus created by computer virus writers will generate a new shape of computer virus automatically for every single iteration and download. This constantly evolving computer virus has caused significant threat to information security of computer users, organizations and even government. However, signature based detection technique which is used by the conventional anti-computer virus software in the market fails to identify it as signatures are unavailable. This research proposed an alternative approach to the traditional signature based detection method and investigated the use of machine learning technique for obfuscated computer virus detection. In this work, text strings are used and have been extracted from virus program codes as the features to generate a suitable classifier model that can correctly classify obfuscated virus files. Text string feature is used as it is informative and potentially only use small amount of memory space. Results show that unknown files can be correctly classified with 99.5% accuracy using SMO classifier model. Thus, it is believed that current computer virus defense can be strengthening through machine learning approach.

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Individual Yarn Fibre Extraction from Micro CT: Multilevel Machine Learning Approach

10.31224/osf.io/m4fjr ◽

2020 ◽

Author(s):

Petr Henys ◽

Lukáš Čapek

Keyword(s):

Machine Learning ◽

Point Of View ◽

Machine Learning Technique ◽

Computational Point ◽

Textile Materials ◽

Machine Learning Approach ◽

Learning Technique ◽

The Individual ◽

Woven Textile ◽

Tomography Data

The internal structure and mechanics of the fibre materials, such as yarn or woven textile, are highly complex. Exploring the fibre structure is an essential step in material engineering either from the experimental or computational point of view. In this study, a new method to extract geometrical and morphological parameters of fibre structures is proposed. The method benefits from standard image analysis and machine learning technique to efficiently extract fibre segments from microcomputer tomography data. The proposed algorithm is tested on the yarn and woven textile materials with different resolution and quality. The developed method can extract the individual fibres with varying accuracy from 73-100% with processing time 2-5s on the tested samples.

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Predicting Dropout in Higher Education: a Systematic Review

10.5753/sbie.2021.217437 ◽

2021 ◽

Author(s):

Jailma Januário da Silva ◽

Norton Trevisan Roman

Keyword(s):

Higher Education ◽

Machine Learning ◽

Systematic Review ◽

Literature Review ◽

Decision Tree ◽

Systematic Literature Review ◽

Machine Learning Technique ◽

Student Dropout ◽

Learning Technique ◽

Research Questions

In this article, we present a systematic literature review, carried out from February to March 2020, on the application of a machine learning technique to predict student dropout in higher education institutions. Besides describing the protocol followed during our research, which includes the research questions, searched databases and query strings, along with criteria for inclusion and exclusion of articles, we also present our main results, in terms of the attributes used by current research on this theme, along with adopted approaches, specific algorithms, and evalution metrics. The Decision Tree technique is the most used for the construction of models, and accuracy and recall and precision being the most used metric for evaluating models.

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A Survey on Machine learning and IoT

ITEJ (Information Technology Engineering Journals) ◽

10.24235/itej.v4i2.51 ◽

2019 ◽

Vol 4 (2) ◽

pp. 99-113

Author(s):

Inkreswari Retno Hardini

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Human Brain ◽

Human Life ◽

Learning Approach ◽

Machine Learning Technique ◽

Machine Learning Approach ◽

Learning Technique ◽

Application Industry

Internet of Things is an intelligence devices that can connect to any other devices. Based on that ability to connect and its intelligence, it makes people to create an intelligence device and capable to learn like human brain. To accomplish that goal, machine learning is the solution. Combination between machine learning and IoT generate a revolution in human life and application industry. The combination also generate new trend in market. Therefore, in this paper will be discuss about machine learning including machine learning technique and algorithm and also discuss about IoT including architecture and elements. Then in this paper will discuss about some researches that have already done that combine IoT with machine learning approach, including issue and challenge in those researches.

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Reinforcement Learning-Based Television White Space Database

Baghdad Science Journal ◽

10.21123/bsj.2021.18.2(suppl.).0947 ◽

2021 ◽

Vol 18 (2(Suppl.)) ◽

pp. 0947

Author(s):

Armie E. Pakzad ◽

Raine Mattheus Manuel ◽

Jerrick Spencer Uy ◽

Xavier Francis Asuncion ◽

Joshua Vincent Ligayo ◽

...

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

High Frequency ◽

Learning Approach ◽

Very High Frequency ◽

Machine Learning Technique ◽

Secondary Users ◽

Machine Learning Approach ◽

Learning Technique ◽

Very High

Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-based TVWSDB. Reinforcement learning (RL) is a machine learning technique that focuses on what has been done based on mapping situations to actions to obtain the highest reward. The learning process was conducted by trying out the actions to gain the reward instead of being told what to do. The actions may directly affect the rewards and future rewards. Based on the results, this algorithm effectively searched the most optimal channel for the SUs in query with the minimum search duration. This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs.

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What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3702749 ◽

2020 ◽

Author(s):

Si Cheng ◽

Ruichang Lu ◽

Xiaojun Zhang

Keyword(s):

Machine Learning ◽

Mutual Fund ◽

Machine Learning Technique ◽

Learning Technique

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The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽

10.3390/atmos11010111 ◽

2020 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 2

Author(s):

Chul-Min Ko ◽

Yeong Yun Jeong ◽

Young-Mi Lee ◽

Byung-Sik Kim

Keyword(s):

Machine Learning ◽

Heavy Rainfall ◽

Extreme Rainfall ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

Machine Learning Technique ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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